KR102655743B1 - Apparatus and method for estimating blood pressure - Google Patents

Abstract

Disclosed is a device for estimating blood pressure. According to one embodiment, the blood pressure estimation device includes a sensor unit that measures bio-signals, acquires feature values related to cardiac output and total peripheral resistance from the bio-signals, and based on the obtained feature values. It may include a processor that determines the blood pressure change mechanism and estimates blood pressure according to the determined blood pressure change mechanism.

Description

Blood pressure estimation device and method {APPARATUS AND METHOD FOR ESTIMATING BLOOD PRESSURE}

This is a technology for cuffless blood pressure estimation.

Recently, research is being conducted on IT-medical convergence technology that combines IT technology and medical technology due to the aging population structure, rapidly increasing medical expenses, and shortage of professional medical service personnel. In particular, monitoring of the human health status is not limited to fixed locations such as hospitals, but has expanded to the field of mobile healthcare, which monitors the user's health status anytime and anywhere in everyday life, such as at home or in the office. It is becoming. Typical types of biological signals that indicate an individual's health status include ECG (Electrocardiography), PPG (Photoplethysmogram), and EMG (Electromyography) signals, and various biological signals are used to measure these in daily life. Signal sensors are being developed. In particular, in the case of the PPG sensor, it is possible to estimate the blood pressure of the human body by analyzing the pulse wave shape that reflects the state of the cardiovascular system.

According to research results related to PPG biosignals, the entire PPG signal is composed of overlapping propagation waves that start from the heart and head toward the extremities of the body and reflection waves that return from the extremities. It is known that information that can estimate blood pressure can be obtained by extracting various features related to traveling or reflected waves.

US Patent Publication US 2016/0081563 (2016.03.24)

A blood pressure estimation device and method is presented that determines the mechanism of blood pressure change when estimating blood pressure and applies an appropriate blood pressure estimation equation according to the mechanism of blood pressure change.

According to one aspect, the blood pressure estimation device acquires a first feature value associated with cardiac output and a second feature value associated with total peripheral resistance from a sensor unit that measures biosignals and biosignals; , may include a processor that determines a blood pressure change mechanism based on at least one of the first feature value and the second feature value, and estimates blood pressure according to the determined blood pressure change mechanism.

At this time, the blood pressure change mechanism may include a general blood pressure change mechanism and a low blood pressure mechanism.

The processor may include a first feature value, a second feature value, a first normalization value obtained by normalizing the first feature value, a second normalization value obtained by normalizing the second feature value, a difference between the first feature value and the second feature value, and a second feature value. At least one of the differences between the first normalized value and the second normalized value may be compared with a preset threshold, and the blood pressure change mechanism may be determined based on the comparison result.

The processor may determine the low blood pressure mechanism if the difference between the first normalized value and the second normalized value is greater than the first threshold, the second normalized value is less than the second threshold, and the second characteristic value is less than the third threshold.

The processor may determine the low blood pressure mechanism if the first normalized value is greater than the fourth threshold and the second normalized value is less than the fifth threshold.

The processor may determine a low blood pressure mechanism if the first characteristic value or the first normalized value is greater than the sixth threshold.

The processor may calculate the first normalization value by dividing the first feature value by the first reference feature value, and calculate the second normalization value by dividing the second feature value by the second reference feature value.

At this time, the first reference feature value or the second reference feature value may be a feature value associated with cardiac output or a feature value associated with total vascular resistance obtained from biosignals measured at the time of calibration.

The processor combines one or more of the heart rate, the shape of the bio-signal waveform, the time and amplitude of the maximum point, the time and amplitude of the minimum point, the time and amplitude of the pulse waveform component positions constituting the bio-signal, and the area of the bio-signal. 1 feature value and a second feature value can be obtained.

The processor calculates a first change amount compared to the reference time point of the first normalized value obtained by normalizing the first feature value and a second change amount compared to the reference time point of the second normalized value obtained by normalizing the second feature value, and the calculated first change amount and the 2 Blood pressure can be estimated based on the amount of change.

If the determined blood pressure change mechanism is a general blood pressure change mechanism, the processor may combine the first change amount and the second change amount and apply a blood pressure estimation equation to the combined result.

If the determined blood pressure change mechanism is a low blood pressure mechanism, the processor may adjust at least one of the first change amount and the second change amount and then combine them, and apply a blood pressure estimation equation to the combined result.

The processor may adjust the first change amount using an attenuation coefficient that attenuates the first change amount, or may adjust the second change amount using an amplification coefficient that amplifies the second change amount.

The processor performs at least one of the first feature value, the second feature value, the first normalization value, the second normalization value, the difference between the first feature value and the second feature value, and the difference between the first normalization value and the second normalization value. The attenuation coefficient or amplification coefficient can be calculated using the attenuation coefficient calculation formula or the amplification coefficient calculation formula using the input value.

The attenuation coefficient calculation formula may be a linear or non-linear function equation that allows the attenuation coefficient to be linearly or non-linearly reduced in at least some sections of the entire change section of the input value.

The amplification coefficient calculation formula may be a linear or non-linear functional formula that allows the amplification coefficient to increase linearly or non-linearly in at least some sections of the entire change section of the input value.

The sensor unit may include a light source that irradiates light to the subject and a detector that detects light scattered from the subject.

Additionally, the blood pressure estimation device may further include an output unit that outputs the processing result of the processor.

According to one aspect, the biometric information estimation method includes the steps of measuring a biosignal, obtaining a first feature value associated with cardiac output and a second feature value associated with total peripheral resistance from the biosignal. It may include a step of determining a blood pressure change mechanism based on at least one of the first feature value and the second feature value, and the step of estimating blood pressure according to the determined blood pressure change mechanism.

The step of determining the mechanism of blood pressure change includes: first feature value, second feature value, first normalized value obtained by normalizing the first feature value, second normalized value obtained by normalizing the second feature value, first feature value, and second feature. At least one of the difference between the values and the difference between the first normalized value and the second normalized value may be compared with a preset threshold, and the blood pressure change mechanism may be determined based on the comparison result.

The step of determining the mechanism of blood pressure change is that if the difference between the first normalized value and the second normalized value is greater than the first threshold, the second normalized value is less than the second threshold, and the second characteristic value is less than the third threshold, hypotension It can be determined by mechanism.

In the step of determining the blood pressure change mechanism, if the first normalized value is greater than the fourth threshold and the second normalized value is less than the fifth threshold, it can be determined as a low blood pressure mechanism.

In the step of determining the blood pressure change mechanism, if the first characteristic value or the first normalized value is greater than the sixth threshold, it can be determined as a low blood pressure mechanism.

In addition, the blood pressure estimation method further includes calculating a first change amount of the first normalized value obtained by normalizing the first feature value compared to the reference time point and a second change amount compared to the reference time point of the second normalized value obtained by normalizing the second feature value. can do.

If the determined blood pressure change mechanism is a general blood pressure change mechanism, the step of estimating blood pressure may include combining the first change amount and the second change amount and applying a blood pressure estimation equation to the combined result.

The step of estimating blood pressure may include, if the determined blood pressure change mechanism is a low blood pressure mechanism, adjusting at least one of the first change amount and the second change amount, combining the adjusted results, and applying a blood pressure estimation equation to the combined result. You can.

In the adjusting step, the first change amount can be adjusted using an attenuation coefficient that attenuates the first change amount, or the second change amount can be adjusted using an amplification coefficient that amplifies the second change amount.

Additionally, the blood pressure estimation method may further include outputting a blood pressure estimation result.

According to one aspect, the blood pressure estimating device includes a sensor unit that measures a biosignal, a first feature value associated with cardiac output obtained from the biosignal, and a second feature value associated with total peripheral resistance. It may include a processor that determines a blood pressure change mechanism based on at least one, obtains an offset value if the determined blood pressure change mechanism is a low blood pressure mechanism, and estimates blood pressure based on the first feature value, the second feature value, and the offset value. there is.

The processor may include a first feature value, a second feature value, a first normalization value obtained by normalizing the first feature value, a second normalization value obtained by normalizing the second feature value, a difference between the first feature value and the second feature value, At least one of the differences between the first normalized value and the second normalized value may be compared with a preset threshold, and the blood pressure change mechanism may be determined based on the comparison result.

The processor sets an offset value based on at least one of the additional feature values obtained from the biosignal, the first change amount of the first normalized value compared to the reference time point, the second change amount of the second normalized value compared to the reference time point, and the change amount of pulse pressure compared to the reference time point. It can be obtained.

If the determined blood pressure change mechanism is a general blood pressure change mechanism, the processor may estimate blood pressure based on the first change amount of the first normalized value compared to the reference time point and the second change amount of the second normalized value compared to the reference time point.

When estimating blood pressure, blood pressure can be accurately estimated by determining the mechanism of blood pressure change and applying an appropriate blood pressure estimation formula according to the mechanism of blood pressure change.

1A and 1B are block diagrams of blood pressure estimation devices according to embodiments.
Figures 2A and 2B are block diagrams of the processors of Figures 1A and 1B.
3A to 3E are diagrams for explaining blood pressure estimation.
Figure 4 is a flowchart of a blood pressure estimation method according to an embodiment.
Figure 5 is a flowchart of a blood pressure estimation method according to another embodiment.
Figures 6a and 6b show wearable devices.
Figure 7 shows a smart device.

Specific details of other embodiments are included in the detailed description and drawings. The advantages and features of the described technology and methods for achieving them will become clear by referring to the embodiments described in detail below along with the drawings. Like reference numerals refer to like elements throughout the specification.

Terms such as first, second, etc. may be used to describe various components, but the components should not be limited by the terms. Terms are used only to distinguish one component from another. Singular expressions include plural expressions unless the context clearly dictates otherwise. Additionally, when a part "includes" a certain component, this means that it may further include other components rather than excluding other components, unless specifically stated to the contrary. In addition, terms such as “… unit” and “module” used in the specification refer to a unit that processes at least one function or operation, which may be implemented as hardware or software, or as a combination of hardware and software.

Hereinafter, embodiments of the blood pressure estimation device and method will be described in detail with reference to the drawings.

1A and 1B are block diagrams of blood pressure estimation devices according to embodiments. The blood pressure estimation devices 100a and 100b of this embodiment may be mounted on terminals such as smartphones, tablet PCs, desktop PCs, and laptop PCs, or on wearable devices that can be worn on the subject. At this time, the wearable device may be implemented as a wrist watch type, bracelet type, wrist band type, ring type, glasses type, or hair band type, but is not limited thereto. It is also possible to be installed in medical devices manufactured for the purpose of measuring and analyzing biometric information in other medical institutions.

Referring to FIGS. 1A and 1B , blood pressure estimating devices 100a and 100b include a sensor unit 110 and a processor 120.

The sensor unit 110 can measure biosignals from the subject. At this time, the biological signal may be a pulse wave signal including a photoplethysmogram (PPG). However, it is not limited to this and may include various biological signals that can be modeled as the sum of a plurality of waveform components, such as electrocardiography (ECG), photoplethysmogram (PPG), and electromyography (EMG) signals. At this time, the subject is a biological area that is in contact with or adjacent to the sensor unit 110 and may be a part of the human body where pulse waves can be easily measured. For example, it may include an area of skin on the wrist adjacent to the radial artery, or an area of human skin through which capillary or venous blood passes. However, it is not limited to this and may be other peripheral parts of the human body, such as fingers and toes, which are areas with high blood vessel density within the human body.

The sensor unit 110 may include a light source and a detector. The light source irradiates light to the subject, and the detector can detect light scattered or reflected from the subject. The light source may include a light emitting diode (LED), a laser diode, a phosphor, etc., and may be formed as one or two or more arrays. The detector includes one or more pixels, and each pixel may include, but is not limited to, a photo diode, a photo transistor, and an image sensor that detects light and converts it into an electrical signal.

The processor 120 may be electrically connected to the sensor unit 110. The processor 120 may control the sensor unit 110 according to a blood pressure estimation request and receive bio-signals from the sensor unit 110. A blood pressure estimation request may be input from the user or may occur when a preset cycle is satisfied. When an electrical bio-signal is received from the sensor unit 110, the processor 120 may perform pre-processing such as filtering to remove noise, amplification of the bio-signal, or conversion to a digital signal.

The processor 120 may estimate blood pressure based on the biosignal received from the sensor unit 110. The processor 120 may determine the mechanism of blood pressure change by analyzing the waveform of the biosignal, and estimate blood pressure by adaptively applying a different blood pressure estimation equation according to the determined mechanism of blood pressure change. At this time, the blood pressure change mechanism may include a general blood pressure change mechanism and a low blood pressure mechanism. Here, the general blood pressure change mechanism may mean a situation in which blood pressure generally increases as cardiac output increases. The hypotension mechanism may refer to a situation in which blood pressure does not increase and low blood pressure occurs even though cardiac output increases, such as when performing high-intensity aerobic exercise.

For example, the processor 120 acquires feature values related to cardiac output (CO) and/or total peripheral resistance (TPR) from biosignals, and changes blood pressure using the obtained feature values. The mechanism can be determined. Here, the characteristic value related to cardiac output may be a characteristic value that shows a tendency to increase/decrease proportionally when there is no significant change in total vascular resistance compared to the resting state, but when cardiac output increases/decreases relatively significantly. Likewise, the characteristic value related to total vascular resistance may be a characteristic value that shows a tendency to increase/decrease proportionally when there is no significant change in cardiac output compared to the resting state, but when total vascular resistance increases/decreases relatively significantly. The processor 120 may determine whether the blood pressure change mechanism at the current time for which blood pressure is to be estimated is a general blood pressure change mechanism or a low blood pressure mechanism by comparing the acquired feature values or values calculated based on the feature values with a preset threshold. there is.

Referring to FIG. 1B, the blood pressure estimating device 100b may further include an output unit 130, a storage unit 140, and a communication unit 150.

The output unit 130 may output results processed by the sensor unit 110 and the processor 120. For example, the output unit 130 may visually output the blood pressure estimate through a display module. Alternatively, it can be output in a non-visual way, such as voice, vibration, or tactile sensation, through a speaker module or haptic module. The output unit 130 can divide the display area into two or more areas according to settings and output a graph of biosignals used for blood pressure estimation, blood pressure estimation results, etc. in the first area, and a graph of the blood pressure estimation history in the second area. It can be output in the following format. At this time, if the estimated blood pressure value is outside the normal range, warning information can be output in various ways, such as highlighting it using red, displaying the normal range together, outputting a voice warning message, and adjusting the vibration intensity.

The storage unit 140 may store the processing results of the sensor unit 110 and the processor 120. Additionally, the storage unit 140 may store various reference information necessary for estimating blood pressure. For example, the reference information may include user characteristic information such as the user's age, gender, health status, etc. In addition, the standard information includes reference blood pressure, blood pressure estimation model, blood pressure estimation cycle, attenuation coefficient calculation formula, amplification coefficient calculation formula, various thresholds for determining blood pressure change mechanism, criteria for determining blood pressure change mechanism, blood pressure estimation method in case of hypotension mechanism, etc. May contain information. However, it is not limited to this.

At this time, the storage unit 140 is a flash memory type, hard disk type, multimedia card micro type, or card type memory (for example, SD or XD memory). etc.), RAM (Random Access Memory: RAM), SRAM (Static Random Access Memory), ROM (Read-Only Memory: ROM), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic It includes, but is not limited to, storage media such as memory, magnetic disk, and optical disk.

The communication unit 150 can communicate with the external device 170 and transmit and receive various data using wired and wireless communication technology under the control of the processor 120. For example, the communication unit 150 can transmit the blood pressure estimation result to the external device 170 and receive various standard information necessary for blood pressure estimation from the external device 170. At this time, the external device may include information processing devices such as a cuff-type blood pressure measuring device, a smartphone, a tablet PC, a desktop PC, and a laptop PC.

At this time, communication technologies include Bluetooth communication, BLE (Bluetooth Low Energy) communication, Near Field Communication (NFC), WLAN communication, Zigbee communication, Infrared Data Association (IrDA) communication, and WFD. (Wi-Fi Direct) communication, UWB (ultra-wideband) communication, Ant+ communication, WIFI communication, RFID (Radio Frequency Identification) communication, 3G communication, 4G communication, and 5G communication. However, it is not limited to this.

2A and 2B are block diagrams of processors according to the embodiments of FIGS. 1A and 1B. 3A to 3E are diagrams for explaining feature extraction for estimating systolic blood pressure and diastolic blood pressure.

Referring to FIG. 2A , the processor 200a may include a feature acquisition unit 210, a blood pressure change mechanism determination unit 220, a blood pressure estimation unit 230, and a feature control unit 240.

It is generally known that blood pressure increases when the human body's cardiac output increases or total vascular resistance increases. Therefore, blood pressure can be estimated using feature values related to cardiac output and total vascular resistance obtained from biosignals. Referring to FIG. 3A, in the general blood pressure change mechanism (GBP), when cardiac output increases, actual blood pressure increases. On the other hand, in situations where blood pressure changes after exercise, the low blood pressure mechanism (LBP) operates, in which actual blood pressure decreases even though cardiac output increases. In this embodiment, by estimating blood pressure by considering these various blood pressure change situations, accurate blood pressure estimation according to the actually operating blood pressure change mechanism is possible.

The feature acquisition unit 210 may acquire feature values by analyzing bio-signals received from the sensor unit 110. At this time, the feature value may include a first feature value associated with cardiac output and a second feature value associated with total vascular resistance.

For example, the feature acquisition unit 210 may obtain the shape of the waveform from the biosignal, the time and amplitude of the maximum point, the time and amplitude of the minimum point, the time and amplitude of the position of the pulse waveform component constituting the biosignal, and one of the biosignals. The first feature value and the second feature value can be obtained by combining one or two or more of the areas of the above sections. At this time, it can be combined in various ways, such as addition, subtraction, division, multiplication, logarithmic values, and combinations thereof, and is not limited to any one in particular. For example, the first feature value may be heart rate (HR), and the second feature value may be the amplitude ratio between each pulse waveform component that constitutes the entire biosignal. However, it is not limited to these examples. At this time, the feature acquisition unit 210 secondly differentiates the biological signal to obtain the positions of the pulse waveform components constituting the biological signal, searches for the minimum point and/or maximum point of the second differential signal, and determines the minimum point and/or maximum point location. can be determined as the pulse waveform component position.

The blood pressure change mechanism determination unit 220 may determine the blood pressure change mechanism using the first characteristic value and/or the second characteristic value. As an example, the blood pressure change mechanism determination unit 220 compares at least one of the first feature value, the second feature value, and the difference between the first feature value and the second feature value with a preset threshold and changes the blood pressure based on the comparison result. The mechanism can be determined. As another example, the first feature value and the second feature value are normalized to obtain a first normalization value and a second normalization value, respectively, and the obtained first normalization value, the second normalization value, and the difference between the first normalization value and the second normalization value are obtained. At least one of the differences may be compared to a threshold. Various other modifications are possible. At this time, the first normalization value and the second normalization value can be obtained by dividing the first feature value and the second feature value by the first reference feature value and the second reference feature value at the time of calibration.

For example, if the difference between the first normalized value and the second normalized value is greater than the first threshold, the second normalized value is less than the second threshold, and the second characteristic value is less than the third threshold, it is determined to be a hypotension mechanism. , Otherwise, it can be judged as a general mechanism of blood pressure change. Alternatively, if the first normalized value is greater than the fourth threshold and the second normalized value is less than the fifth threshold, it can be determined as a low blood pressure mechanism, and otherwise, it can be determined as a general blood pressure change mechanism. Alternatively, if the first characteristic value or the first normalized value is greater than the preset sixth threshold, it may be determined that a hypotension mechanism has occurred. However, it is not limited to this decision example, and may be modified in various ways depending on user characteristics, computing performance of the applied device, etc. Additionally, each threshold is a value that is appropriately set through a preprocessing process and may be a personalized value for each user or a value set for each group. Additionally, it may be preset to suit each device, taking into account the bio-signal distribution characteristics obtained from each device.

The blood pressure estimation unit 230 may estimate blood pressure according to the determined blood pressure change mechanism. For blood pressure estimation, the blood pressure estimation unit 230 may calculate a first change amount compared to a reference time point, such as a calibration time point, for the first normalized value, and calculate a second change amount compared to the reference time point for the second normalized value. Equation 1 below gives an example of calculating the first change amount and the second change amount.

Here, Δf ₁ is the first change amount, f _1cur is the first feature value, f _1cal is the first reference feature value, Δf ₂ is the second change amount, and f _2cur is The second feature value, f _2cal , means the second reference feature value.

If the determined blood pressure change mechanism is a general blood pressure change mechanism, the blood pressure estimation unit 230 may estimate blood pressure by combining the first change amount and the second change amount and applying a blood pressure estimation equation to the combined result. For example, a blood pressure estimation equation such as Equation 2 below can be applied. The blood pressure estimation equation in Equation 2 is a linear function, but is not limited thereto.

Here, BP means estimated blood pressure, Δf ₁ means the first change amount, and Δf ₂ means the second change amount. In addition, A and B are predefined values, where A is a scale factor for scaling the value combining the first change amount and the second change amount, and B is the reference blood pressure at the time of calibration, for example, a cuff blood pressure device, etc. It may be actual blood pressure measured using .

If the determined blood pressure change mechanism is a low blood pressure mechanism, the blood pressure estimation unit 230 adjusts the first change amount and/or the second change amount through the feature control unit 240, and uses the adjusted first change amount and/or the second change amount. This allows you to estimate blood pressure.

The feature control unit 240 may attenuate the first amount of change using an attenuation coefficient that attenuates the first amount of change. Additionally, the second change amount can be amplified using an amplification coefficient that amplifies the second change amount. At this time, both the attenuation of the first change amount and the amplification of the second change amount may be performed depending on the setting, or only one of them may be performed. In other words, the estimated blood pressure can be reduced in accordance with the hypotension mechanism by reducing the width of the first change associated with cardiac output using the attenuation coefficient and increasing the width of the second change associated with total vascular resistance using the amplification coefficient. there is.

For example, the feature control unit 240 may calculate the attenuation coefficient using the attenuation coefficient calculation formula. Additionally, the amplification coefficient can be calculated using the amplification coefficient calculation formula. At this time, the attenuation coefficient calculation formula and the amplification coefficient calculation formula are predefined and include the first characteristic value, the second characteristic value, the first normalized value, the second normalized value, the difference between the first characteristic value and the second characteristic value, and the first normalized value. It may be a linear or non-linear function that takes as input at least one of the difference between and the second normalized value and the heart rate.

Referring to FIGS. 3B and 3C, the attenuation coefficient calculation formula may be defined so that the attenuation coefficient decreases linearly or non-linearly in at least some sections of the entire change section of the input value. As an example, referring to FIG. 3b, the attenuation coefficient calculation formula is a linear function equation to set the attenuation coefficient (y ₁ ) to 1 so that the first change amount does not change in the section from 0 to a ₁ on the X-axis, and to decrease linearly from a ₁ to a ₂ It is set to , and from a2 onwards, it can be fixed to _a3 so that it does not decrease further. As another example, referring to Figure 3c, the attenuation coefficient calculation formula sets the attenuation coefficient (y ₁ ) to 1 so that the first change amount does not decrease in the section from 0 to u ₁ on the X-axis, and from u ₁ to u ₃ the first change amount is nonlinear. but is defined as a non-linear functional formula so that the attenuation coefficient (y ₁ ) is 1, u ₄ , and u ₅ at u ₁ , u _{2 , and} u ₃ , respectively, and the attenuation coefficient (y ₁ ) is set so that it does not decrease further from u ₃ onwards. It can be fixed as u ₅ .

Likewise, referring to FIGS. 3D and 3E, the amplification coefficient calculation formula may be defined as a linear function or a non-linear function so that the amplification coefficient increases linearly or non-linearly in at least some sections of the entire change section (X-axis) of the input value. That is, as shown in Figure 3d, the amplification coefficient (y ₂ ) is set to 1 so that the second change amount does not change in the section from ₀ to a _{1 on} the It is defined and can be fixed to a ₃ so that it no longer increases from a2. In addition, as shown in Figure 3e, the amplification coefficient calculation formula sets the amplification coefficient (y ₂ ) to 1 so that the second change amount does not change in the section from 0 to u ₁ , and increases non-linearly from u ₁ to u ₃ , but u ₁ , In u _{2 and} u ₃ , the amplification coefficient (y ₂ ) is defined as a non-linear function to have 1, u ₄ , and u ₅ , respectively, and can be fixed to u ₅ so that it does not increase further from u ₃ onwards.

When the first change amount and/or the second change amount are adjusted by the feature control unit 240, the blood pressure estimation unit 230 can estimate blood pressure by applying a blood pressure estimation equation such as Equation 3 below.

Here, BP means estimated blood pressure, Δf ₁ means the first change amount, and Δf ₂ means the second change amount. In addition, A and B are predefined values, where A is a scale factor for scaling the value combining the first change amount and the second change amount, and B is the reference blood pressure at the time of calibration, for example, a cuff blood pressure device, etc. It may be actual blood pressure measured using . y ₁ and y ₂ refer to the attenuation coefficient and amplification coefficient calculated by the feature control unit 240.

Meanwhile, referring to FIG. 2B, the processor 200b according to another embodiment may include an offset acquisition unit 250 instead of the feature adjustment unit 240.

According to this embodiment, if the determined blood pressure change mechanism is a low blood pressure mechanism, the blood pressure estimation unit 230 may request the offset acquisition unit 250 to obtain an offset value to be additionally reflected in the blood pressure estimation. At this time, the offset may be a value for correcting the error between the estimated blood pressure and the actual blood pressure that occurs when the blood pressure is estimated using a blood pressure estimation equation such as Equation 1 in the hypotension mechanism.

The offset acquisition unit 250 may acquire an offset value using additional feature values obtained from bio-signals, the first change amount, the second change amount, and the change amount of pulse pressure compared to the reference point. For example, a third change amount can be obtained by multiplying the first change amount and the second change amount, and an offset value can be obtained using the third change amount. Alternatively, heart rate (HR) obtained through PTT (Pulse Transit Time) and HRV (Heart rate variation) analysis of measured biosignals can be used as a feature value related to pulse pressure. For example, a characteristic value related to pulse pressure may be normalized to a characteristic value related to pulse pressure at the time of calibration, a fourth change amount compared to the time of calibration may be calculated, and an offset value may be obtained using the calculated fourth change amount. However, it is not limited to these examples, and various values that can reflect actual changes in blood pressure can be obtained as offset values in the hypotension mechanism.

The offset acquisition unit 250 may obtain the third change amount or the fourth change amount as an offset value. Alternatively, if there is a predefined offset estimation model, the third or fourth change amount can be input into the offset estimation model, and the value output from the offset estimation model can be obtained as the offset value. At this time, the offset estimation model can be predefined in consideration of various measurement conditions, such as the computing performance of the blood pressure estimation device, the type of biosignal measurement sensor mounted on the device, and the user's characteristics.

When an offset value that can reflect the blood pressure change situation in which the low blood pressure mechanism operates is obtained by the offset acquisition unit 250, the blood pressure estimation unit 230 estimates blood pressure by applying a blood pressure estimation equation such as Equation 4 below. You can.

Here, BP means estimated blood pressure, Δf ₁ means the first change amount, and Δf ₂ means the second change amount. In addition, A and B are predefined values, where A is a scale factor for scaling the value combining the first change amount and the second change amount, and B is the reference blood pressure at the time of calibration, for example, a cuff blood pressure device, etc. It may be actual blood pressure measured using . Offset may be an offset value obtained by the offset acquisition unit 250.

Referring again to FIGS. 2A and 2B, the blood pressure estimator 230 can independently estimate the average blood pressure, diastolic blood pressure, and systolic blood pressure using the blood pressure estimation equations of Equations 2 to 4 above. For this purpose, A in Equations 2 to 4 can be set to an appropriate value for each average blood pressure, diastolic blood pressure, and systolic blood pressure. Alternatively, the average blood pressure, diastolic blood pressure, and systolic blood pressure can be independently estimated by applying appropriate weights to each change in Equations 2 to 4 according to the type of blood pressure and then combining them.

Alternatively, the blood pressure estimation unit 230 may sequentially estimate the average blood pressure, diastolic blood pressure, and systolic blood pressure. For example, the blood pressure estimation unit 230 first estimates the average blood pressure using Equations 2 to 4 above, and then calculates the diastolic blood pressure and systolic blood pressure using the estimated average blood pressure and pulse pressure as shown in Equations 5 and 6 below. can be estimated.

Here, MAP means estimated average blood pressure, DBP means diastolic blood pressure, and SBP means systolic blood pressure. Additionally, PP stands for pulse pressure and HR stands for heart rate.

Figure 4 is a flowchart of a blood pressure estimation method according to an embodiment. The embodiment of FIG. 4 is an example of a blood pressure estimation method performed by the blood pressure estimation devices 100a and 100b of FIG. 1A or 1B.

First, when the blood pressure estimation device receives a blood pressure estimation request, it can measure biosignals (410). A blood pressure estimation device may provide an interface for performing various interactions with a user. A user may request blood pressure estimation through an interface provided by the blood pressure estimation device. Alternatively, a blood pressure estimation request may be received from an external device. At this time, the blood pressure estimation request received from the external device may include a request to provide a blood pressure estimation result. If the external device is equipped with a blood pressure estimation algorithm, it may include a request for provision of acquired features. External devices may include smartphones, tablet PCs, laptop PCs, and wearable devices carried by the user. The blood pressure estimating device can measure biosignals including pulse wave signals from a subject by controlling a sensor.

Next, the biosignals can be analyzed to obtain a first feature value related to cardiac output and a second feature value related to total vascular resistance (420). For example, the first feature value may be heart rate, and the second feature value may be the amplitude ratio of each pulse waveform component constituting the biosignal. However, it is not limited to this and may be a value that is an appropriate combination of one or two or more of the time/amplitude of the maximum/minimum point of the biosignal, the time/amplitude of the constituent pulse waveform position, the area of the biosignal, and heart rate. At this time, the method of combining two or more is not particularly limited.

Next, the amount of change in the first feature value and the amount of change in the second feature value compared to the reference time point can be obtained (430). For example, the first normalized value may be calculated by dividing the first feature value by the first reference feature value at the time of calibration, and the first change amount may be calculated using the calculated first normalized value. Additionally, the second normalized value may be calculated by dividing the second feature value by the second reference feature value at the time of calibration, and the first change amount may be calculated using the calculated second normalized value.

Next, the mechanism of blood pressure change can be determined (440). At this time, the first feature value and the second feature value can be used as is, or the first normalization value and the second normalization value obtained by normalizing the first feature value and the second feature value can be used. Alternatively, the values can be appropriately combined to calculate a new value and use the calculated value. For example, two or more values can be compared with thresholds set for each, and if all conditions are satisfied, it can be determined to be a hypotension mechanism. Alternatively, it is possible to determine whether it is a hypotension mechanism by comparing only one value with the threshold. A detailed explanation has been described above.

Next, if the low blood pressure mechanism is determined (440), the first amount of change and/or the second amount of change can be adjusted (450). At this time, the first change amount can be attenuated using an attenuation coefficient, or the second change amount can be amplified using an amplification coefficient. The attenuation coefficient or amplification coefficient can be obtained using a preset calculation formula as described above. Attenuation of the first change amount and amplification of the second change amount may be performed together or alone.

Next, when the first change amount or the second change amount is adjusted in step 450, the blood pressure can be estimated using the adjusted first change amount and the second change amount (460). If the general blood pressure change mechanism is determined in step 440, the blood pressure can be estimated using the first change amount and the second change amount without adjusting the first change amount and the second change amount (460). A detailed explanation has been described above and is thus omitted.

Next, the blood pressure estimation result can be output (470). For example, blood pressure estimation results can be output in various visual ways through visual output means such as a display. Alternatively, it can be provided to the user in a non-visual way such as voice, touch, or vibration through a speaker and/or a haptic module. Additionally, the user's health status can be determined based on the estimated biometric information, and warnings or countermeasures can be provided to the user based on the judgment results.

Figure 5 is a flowchart of a blood pressure estimation method according to another embodiment. The embodiment of FIG. 5 is an example of a blood pressure estimation method performed by the blood pressure estimation devices 100a and 100b of FIG. 1A or 1B.

First, when the blood pressure estimation device receives a blood pressure estimation request, it can measure bio-signals (510) and analyze the bio-signals to obtain a first feature value associated with cardiac output and a second feature value associated with total vascular resistance ( 520).

Next, the amount of change in the first feature value and the amount of change in the second feature value compared to the reference point are obtained (530), and the mechanism of blood pressure change can be determined (540).

Next, if the low blood pressure mechanism is determined (540), in a blood pressure change situation in which the low blood pressure mechanism operates, an offset is obtained to correct the error between the estimated blood pressure and the actual blood pressure using the first change amount and the second change amount (550). , blood pressure can be estimated using the first change amount, the second change amount, and the offset (560). For example, the offset can be obtained using a value obtained by multiplying the first change amount and the second change amount and a characteristic value related to pulse pressure obtained from a biosignal, such as heart rate. At this time, a predefined offset estimation model may be used.

If the general blood pressure change mechanism is determined in step 540, the blood pressure can be estimated using the first change amount and the second change amount without obtaining an additional offset (570).

Next, the blood pressure estimation result can be output (580). For example, it can be provided to the user in various visual ways through visual output means such as a display, or in non-visual ways such as voice, touch, and vibration through speakers and/or haptic modules.

Figures 6a and 6b show wearable devices. Embodiments of the above-described blood pressure estimation devices 100a and 100b may be mounted on a smart watch or smart band-type wearable device worn on the wrist. However, this is only an example for convenience of explanation, and can also be applied to information processing terminals such as smartphones, tablet PCs, laptop PCs, and desktop PCs.

Referring to FIGS. 6A and 6B , the wearable device 600 may include a device body 610 and a strap 620.

The main body 610 may be formed to have various shapes, and modules for performing the above-described blood pressure estimation function and various other functions may be mounted on its interior or surface. A battery that supplies power to various modules of the device 600 may be built into the main body 610 or the strap 620.

Strap 620 may be connected to main body 610. The strap 620 may be flexible so that it can be bent into a shape that surrounds the user's wrist. The strap 620 may be detached from the user's wrist or may be configured as a band that is not detachable. The strap 620 may have air injected therein or may include an air bladder to have elasticity according to changes in pressure applied to the wrist, and may transmit changes in pressure of the wrist to the main body 610.

The main body 610 may be equipped with a sensor unit 620 that measures biological signals. The sensor unit 620 may be mounted on the rear of the main body 610, where the upper part of the user's wrist is in contact, and may include a light source that irradiates light to the wrist skin and a detector that detects light scattered or reflected from the subject. there is. The sensor unit 620 may further include a contact pressure sensor that measures contact pressure applied by the subject.

A processor may be mounted inside the main body 610, and the processor 610 may be electrically connected to various components mounted on the wearable device 600 and control the various components.

Additionally, the processor can estimate blood pressure using the biosignal measured by the sensor unit 620. The processor may obtain a first feature value related to cardiac output and a second feature value related to total vascular resistance from the biosignal. Additionally, the processor may obtain the first normalization value and the second normalization value by normalizing the first feature value and the second feature value to the first reference feature value and the second reference feature value at the time of calibration. Additionally, the first change amount and the second change amount can be obtained for the first normalized value and the second normalized value compared to the calibration point, and the obtained first change amount and the second change amount can be combined to estimate blood pressure.

Meanwhile, the processor may determine whether the blood pressure change mechanism is a low blood pressure mechanism using the acquired values. For example, if the difference between the first normalized value and the second normalized value is greater than the first threshold, the second normalized value is less than the second threshold, and the second characteristic value is less than the third threshold, it can be determined as a hypotension mechanism. You can. However, it is not limited to this.

If the determined blood pressure change mechanism is a general blood pressure change mechanism, the processor can estimate blood pressure by combining the first change amount and the second change amount as shown in Equation 1 above. If it is determined that the blood pressure change mechanism is a hypotension mechanism, an offset value is additionally obtained to correct the error between the actual blood pressure and the estimated blood pressure according to the hypotension mechanism, and an offset value is added in addition to the first change amount and the second change amount as shown in Equation 4 above. By further combining, blood pressure can be estimated.

When a contact pressure sensor is mounted, the processor can monitor the contact state of the subject based on the contact pressure between the wrist and the sensor unit 620 and guide the user to the contact position and/or contact state through the display unit.

Additionally, a storage unit that stores processing results of the processor and various information may be installed inside the main body 610. At this time, the various information may include various information related to the function of the wearable device 600 in addition to standard information for estimating blood pressure.

Additionally, a manipulation unit 640 that receives a user's control command and transmits it to the processor may be mounted on the main body 610. The manipulation unit 640 may include a power button for inputting a command to turn on/off the wearable device 600.

The display unit 614 may be mounted on the front of the main body 610 and may include a touch panel capable of touch input. The display unit 614 may receive the user's touch input, transmit it to the processor, and display the processing result of the processor.

For example, the display unit 614 may display estimated blood pressure information. At this time, additional information such as blood pressure estimation date or health status can be displayed. At this time, when the user operates the manipulation unit 640 or requests detailed information through a touch input on the display unit 614, the detailed information can be output in various ways.

Referring to FIG. 6B, the display unit 614 may output detailed information in the first area 614a and a blood pressure history graph in the second area 614b. At this time, the blood pressure history graph may include objects (e.g., shapes such as circles and squares) representing blood pressure estimation points. Additionally, an identification mark (M) indicating the object (I) currently selected by the user may be displayed on the blood pressure history graph. The identification mark (M) is shown as a vertical line, but is not limited to this and may be displayed in various forms such as polygons such as circles and squares, and arrows pointing to its location. The user may request display of a blood pressure history graph, and when the blood pressure history graph is displayed in the second area 614b, touch the object (I) at a specific point in time or move the graph left or right to select the object (I) at a specific point in time. By matching the identification mark (M), detailed information can be output to the first area 614a. At this time, information such as the estimated blood pressure value at the estimated time point selected by the user, the estimated date, and health status at that time can be output to the first area 614a. However, it is not limited to this.

Additionally, a communication unit for communicating with an external device such as a user's portable terminal may be installed inside the main body 610. The communication unit may transmit the biometric information estimation result to an external device, such as the user's smartphone, and display it to the user. However, without being limited thereto, various necessary information can be transmitted and received.

Figure 7 shows a smart device to which embodiments of a blood pressure estimation device are applied. At this time, smart devices may include smartphones and tablet PCs.

Referring to FIG. 7 , the smart device 700 may have a sensor unit 730 mounted on one surface of the main body 710. The sensor unit 730 may include a pulse wave sensor including one or more light sources 731 and a detector 732. As shown, the sensor unit 730 may be mounted on the rear of the main body 710, but is not limited to this, and it is also possible to form the sensor unit 730 by combining it with a fingerprint sensor or a touch panel on the front.

Additionally, a display unit may be mounted on the front of the main body 710. The display unit can visually output biometric information estimation results, etc. The display unit may include a touch panel, and may receive various information input through the touch panel and transmit it to the processor.

Meanwhile, an image sensor 720 may be mounted on the main body 710. When a user approaches the sensor unit 730 with a finger to measure a pulse wave signal, the image sensor 720 may photograph the finger and transmit the image to the processor. At this time, the processor determines the relative position of the finger compared to the actual position of the sensor unit 730 from the image of the finger, and provides relative position information of the finger to the user through the display unit, thereby guiding the pulse wave signal to be measured more accurately. .

The processor can estimate blood pressure using the biosignal measured by the sensor unit 730. The processor may determine whether the blood pressure change mechanism is a general blood pressure change mechanism or a low blood pressure change mechanism, and estimate blood pressure in an appropriate manner according to the determined blood pressure change mechanism. For example, the difference between the above-described first normalized value associated with cardiac output and the second normalized value associated with total vascular resistance is greater than the first threshold, the second normalized value is less than the second threshold, and the second characteristic value is If it is less than the third threshold, it can be judged as a hypotension mechanism. However, it is not limited to this.

If the determined blood pressure change mechanism is a general blood pressure change mechanism, the processor can estimate blood pressure by combining the first change amount and the second change amount as shown in Equation 1 above. If it is determined that the blood pressure change mechanism is a low blood pressure mechanism, the first change amount and/or the second change amount are appropriately adjusted as described above, and then the adjusted first change amount and the second change amount are combined as in Equation 3 above. This allows you to estimate blood pressure. For example, for the first change amount, the first change amount is adjusted after obtaining the attenuation coefficient by applying the attenuation coefficient calculation formula shown in Figure 3b, and for the second change amount, the second change amount is adjusted by setting the amplification coefficient to 1. It may not be possible. However, it is not limited to this.

Meanwhile, these embodiments can be implemented as computer-readable codes on a computer-readable recording medium. Computer-readable recording media include all types of recording devices that store data that can be read by a computer system.

Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage devices, and can also be implemented in the form of a carrier wave (e.g., transmission via the Internet). Includes. Additionally, the computer-readable recording medium can be distributed across computer systems connected to a network, so that computer-readable code can be stored and executed in a distributed manner. And functional programs, codes, and code segments for implementing the present embodiments can be easily deduced by programmers in the technical field to which the present invention pertains.

A person skilled in the art to which this disclosure pertains will understand that it can be implemented in other specific forms without changing the disclosed technical idea or essential features. Therefore, the embodiments described above should be understood in all respects as illustrative and not restrictive.

100a, 100b: Blood pressure estimation device 110: Sensor unit
120: Processor 130: Output unit
140: storage unit 150: communication unit
200a, 200b: Processor 210: Feature acquisition unit
220: Blood pressure change mechanism determination unit 230: Blood pressure estimation unit
240: Feature control unit 250: Offset acquisition unit
600: Wearable device 610: Body
614: display unit 620: sensor unit
630: Strap 640: Control panel
700: Smart device 710: Body
720: Image sensor 730: Sensor unit
731: Light source 732: Detector

Claims (32)

A sensor unit that measures biological signals; and
Obtain a first feature value associated with cardiac output and a second feature value associated with total peripheral resistance from the biosignal, and obtain the first feature value, the second feature value, and the first feature value. At least one of the first normalized value normalized, the second normalized value normalized to the second feature value, the difference between the first feature value and the second feature value, and the difference between the first normalization value and the second normalization value is preset. Compare with a threshold, determine a blood pressure change mechanism based on the comparison result, calculate a first change amount of the first normalized value compared to the reference point and a second change amount of the second normalized value compared to the reference point, and calculate the determined blood pressure change If the mechanism is a general blood pressure change mechanism, the first change amount and the second change amount are combined, and if the determined blood pressure change mechanism is a low blood pressure mechanism, a processor for estimating blood pressure by adjusting at least one of the first change amount and the second change amount and then combining them. A blood pressure estimation device comprising: delete delete According to paragraph 1,
The processor is
If the difference between the first normalized value and the second normalized value is greater than the first threshold, the second normalized value is less than the second threshold, and the second characteristic value is less than the third threshold, a hypotension mechanism is determined, and the first The threshold, the second threshold, and the third threshold are different from each other. According to paragraph 1,
The processor is
If the first normalized value is greater than the fourth threshold and the second normalized value is less than the fifth threshold, it is determined to be a hypotension mechanism, and the fourth and fifth thresholds are different from each other. According to paragraph 1,
The processor is
A blood pressure estimation device that determines a low blood pressure mechanism when the first characteristic value or the first normalized value is greater than the sixth threshold. According to paragraph 1,
The processor is
A blood pressure estimation device that calculates a first normalized value by dividing a first feature value by a first reference feature value, and calculates a second normalized value by dividing a second feature value by a second reference feature value. In clause 7,
The first reference feature value or the second reference feature value is
A blood pressure estimation device that is a feature value related to cardiac output or a feature value related to total vascular resistance obtained from biosignals measured at the time of calibration. According to paragraph 1,
The processor is
By combining one or more of the following: heart rate, the shape of the bio-signal waveform, the time and amplitude of the maximum point, the time and amplitude of the minimum point, the time and amplitude of the pulse waveform component positions constituting the bio-signal, and the area of the bio-signal A blood pressure estimation device that acquires a first feature value and a second feature value. delete delete delete According to paragraph 1,
The processor is
A blood pressure estimation device that adjusts a first change amount using an attenuation coefficient that attenuates the first change amount, or adjusts a second change amount using an amplification coefficient that amplifies the second change amount. According to clause 13,
The processor is
At least one of the first feature value, the second feature value, the first normalization value, the second normalization value, the difference between the first feature value and the second feature value, and the difference between the first normalization value and the second normalization value is an input value. Calculate the attenuation coefficient or amplification coefficient using the attenuation coefficient calculation formula or the amplification coefficient calculation formula,
The attenuation coefficient calculation formula is
The attenuation coefficient is a linear or non-linear function defined to decrease linearly or non-linearly in at least a portion of the entire change section of the input value,
The amplification coefficient calculation formula is
A blood pressure estimation device wherein the amplification coefficient is a linear or non-linear function defined to increase linearly or non-linearly in at least some sections of the entire change section of the input value. delete delete According to paragraph 1,
The sensor unit
A light source that radiates light to the subject; and
A blood pressure estimation device including a detector that detects light scattered from the subject. According to paragraph 1,
A blood pressure estimation device further comprising an output unit that outputs a processing result of the processor. blood pressure estimation device,
Measuring biosignals;
Obtaining a first feature value associated with cardiac output and a second feature value associated with total peripheral resistance from the biosignal;
calculating a first change amount of a first normalized value obtained by normalizing the first feature value compared to a reference point in time and a second change amount of a second normalized value obtained by normalizing the second feature value compared to a reference point in time;
At least one of the first feature value, the second feature value, the first normalization value, the second normalization value, the difference between the first feature value and the second feature value, and the difference between the first normalization value and the second normalization value is preliminarily determined. Comparing with a set threshold and determining a mechanism for changing blood pressure based on the comparison result; and
If the determined blood pressure change mechanism is a general blood pressure change mechanism, the first change amount and the second change amount are combined, and if the determined blood pressure change mechanism is a hypotension mechanism, at least one of the first change amount and the second change amount is adjusted and then combined. A blood pressure estimation method including the step of estimating. delete According to clause 19,
The step of determining the mechanism of blood pressure change is
If the difference between the first normalized value and the second normalized value is greater than the first threshold, the second normalized value is less than the second threshold, and the second characteristic value is less than the third threshold, a hypotension mechanism is determined, and the first A method of estimating blood pressure, wherein the threshold, the second threshold and the third threshold are different from each other. According to clause 19,
The step of determining the mechanism of blood pressure change is
If the first normalized value is greater than the fourth threshold and the second normalized value is less than the fifth threshold, it is determined to be a hypotension mechanism, and the fourth and fifth thresholds are different from each other. According to clause 19,
The step of determining the mechanism of blood pressure change is
A blood pressure estimation method that determines a low blood pressure mechanism when the first characteristic value or the first normalized value is greater than the sixth threshold. delete delete delete According to clause 19,
The adjustment step is
A blood pressure estimation method that adjusts a first change amount using an attenuation coefficient that attenuates the first change amount, or adjusts a second change amount using an amplification coefficient that amplifies the second change amount. According to clause 19,
A blood pressure estimation method further comprising outputting the blood pressure estimation result. A sensor unit that measures biological signals; and
A first feature value associated with cardiac output obtained from the biosignal and a second feature value associated with total peripheral resistance, a first normalization value obtained by normalizing the first feature value, and the second feature value. At least one of the second normalized value obtained by normalizing the feature value, the difference between the first feature value and the second feature value, and the difference between the first normalization value and the second normalization value is compared with a preset threshold, and the blood pressure is based on the comparison result. Determine the change mechanism, calculate a first change amount compared to the reference point of the first normalized value and a second change amount compared to the reference point of the second normalized value, and if the determined blood pressure change mechanism is a low blood pressure mechanism, an offset value is obtained to obtain the first change amount compared to the reference time point of the first normalized value. A blood pressure estimation device comprising a processor that estimates blood pressure by combining the first change amount, the second change amount, and the offset value. delete According to clause 29,
The processor is
An offset value based on at least one of the additional feature values obtained from the biosignal, the first change amount of the first normalized value compared to the reference time point, the second change amount of the second normalized value compared to the reference time point, and the change amount of pulse pressure compared to the reference time point. A blood pressure estimation device that obtains. According to clause 29,
The processor is
When the determined blood pressure change mechanism is a general blood pressure change mechanism, a blood pressure estimation device for estimating blood pressure based on a first change amount of the first normalized value compared to the reference point and a second change amount of the second normalized value compared to the reference point.

Images